Peter Sykacek

2.8k total citations
36 papers, 827 citations indexed

About

Peter Sykacek is a scholar working on Molecular Biology, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Peter Sykacek has authored 36 papers receiving a total of 827 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 9 papers in Artificial Intelligence and 4 papers in Signal Processing. Recurrent topics in Peter Sykacek's work include Gene expression and cancer classification (7 papers), Bioinformatics and Genomic Networks (6 papers) and Neural Networks and Applications (5 papers). Peter Sykacek is often cited by papers focused on Gene expression and cancer classification (7 papers), Bioinformatics and Genomic Networks (6 papers) and Neural Networks and Applications (5 papers). Peter Sykacek collaborates with scholars based in Austria, United Kingdom and Australia. Peter Sykacek's co-authors include Stephen Roberts, María Stokes, David P. Kreil, Florian Wagner, Florian M. W. Grundler, Dagmar Szakasits, Julia Hofmann, Holger Bohlmann, Krzysztof Wieczorek and Eleanor Curran and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Nature Biotechnology.

In The Last Decade

Peter Sykacek

34 papers receiving 783 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Peter Sykacek Austria 14 292 201 197 115 95 36 827
Thomas Lingner Germany 25 1.1k 3.7× 143 0.7× 371 1.9× 200 1.7× 67 0.7× 49 2.0k
Bruno da Silva Belgium 14 283 1.0× 50 0.2× 169 0.9× 292 2.5× 46 0.5× 87 1.1k
Philippe Tarroux France 16 462 1.6× 89 0.4× 76 0.4× 80 0.7× 56 0.6× 38 957
Xin Deng China 15 335 1.1× 49 0.2× 193 1.0× 87 0.8× 46 0.5× 67 736
Éric Le Carpentier France 17 450 1.5× 116 0.6× 92 0.5× 124 1.1× 21 0.2× 45 967
Marco Vilela United States 15 420 1.4× 110 0.5× 127 0.6× 243 2.1× 23 0.2× 20 806
Subhadip Basu India 22 417 1.4× 30 0.1× 51 0.3× 96 0.8× 298 3.1× 130 1.8k
Wolfgang Mader Germany 11 192 0.7× 43 0.2× 384 1.9× 85 0.7× 34 0.4× 23 748
Xin Zhou United States 20 428 1.5× 34 0.2× 238 1.2× 89 0.8× 54 0.6× 81 1.0k
Joël Ryan Canada 17 400 1.4× 66 0.3× 85 0.4× 105 0.9× 7 0.1× 35 756

Countries citing papers authored by Peter Sykacek

Since Specialization
Citations

This map shows the geographic impact of Peter Sykacek's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Peter Sykacek with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Sykacek more than expected).

Fields of papers citing papers by Peter Sykacek

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter Sykacek. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Peter Sykacek. The network helps show where Peter Sykacek may publish in the future.

Co-authorship network of co-authors of Peter Sykacek

This figure shows the co-authorship network connecting the top 25 collaborators of Peter Sykacek. A scholar is included among the top collaborators of Peter Sykacek based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Peter Sykacek. Peter Sykacek is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Weigl, Moritz, Katharina Schimek, Barbara Schädl, et al.. (2024). Analysis of extracellular vesicle microRNA profiles reveals distinct blood and lymphatic endothelial cell origins. SHILAP Revista de lepidopterología. 3(1). e134–e134. 2 indexed citations
2.
Wagner, Anja, et al.. (2022). Identification of Activating Mutations in the Transmembrane and Extracellular Domains of EGFR. Biochemistry. 61(19). 2049–2062. 4 indexed citations
3.
Fossati, Andrea, Chen Li, Federico Uliana, et al.. (2021). PCprophet: a framework for protein complex prediction and differential analysis using proteomic data. Nature Methods. 18(5). 520–527. 43 indexed citations
4.
Mehnen, Lars, et al.. (2021). Investigating Explanatory Factors of Machine Learning Models for Plant Classification. Plants. 10(12). 2674–2674. 8 indexed citations
5.
Curto, Manuel, Paul Meulenbroek, Esayas Alemayehu, et al.. (2021). Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning. PLoS ONE. 16(4). e0249593–e0249593. 7 indexed citations
6.
Sykacek, Peter, David P. Kreil, Lisa Meadows, et al.. (2011). The impact of quantitative optimization of hybridization conditions on gene expression analysis. BMC Bioinformatics. 12(1). 73–73. 9 indexed citations
7.
Minois, Nadège, et al.. (2010). RNA Interference in Ageing Research – A Mini-Review. Gerontology. 56(5). 496–506. 9 indexed citations
8.
Leparc, Germán, et al.. (2008). Model-based probe set optimization for high-performance microarrays. Nucleic Acids Research. 37(3). e18–e18. 21 indexed citations
9.
Szakasits, Dagmar, Krzysztof Wieczorek, Julia Hofmann, et al.. (2008). The transcriptome of syncytia induced by the cyst nematode Heterodera schachtii in Arabidopsis roots. The Plant Journal. 57(5). 771–784. 183 indexed citations
10.
Burnstein, Rowan, Xiaoling He, Richard Luce, et al.. (2007). Gene expression changes in long term expanded human neural progenitor cells passaged by chopping lead to loss of neurogenic potential in vivo. Experimental Neurology. 204(2). 512–524. 49 indexed citations
11.
Rezek, Iead, Stephen Roberts, & Peter Sykacek. (2003). Ensemble Coupled Hidden Markov Models for Joint Characterisation of Dynamic Signals. International Conference on Artificial Intelligence and Statistics. 233–239. 2 indexed citations
12.
Sykacek, Peter, et al.. (2003). Probabilistic methods in BCI research. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 11(2). 192–194. 26 indexed citations
13.
Sykacek, Peter & Stephen Roberts. (2002). Adaptive Classification by Variational Kalman Filtering. Oxford University Research Archive (ORA) (University of Oxford). 15. 753–760. 17 indexed citations
14.
Sykacek, Peter & Stephen Roberts. (2001). Bayesian time series classification. Oxford University Research Archive (ORA) (University of Oxford). 14. 937–944. 20 indexed citations
15.
Rezek, Iead, Peter Sykacek, & Stephen Roberts. (2000). Learning interaction dynamics with coupledhidden Markovmodels. IEE Proceedings - Science Measurement and Technology. 147(6). 345–350. 18 indexed citations
16.
Sykacek, Peter. (1999). On Input Selection with Reversible Jump Markov Chain Monte Carlo Sampling. Neural Information Processing Systems. 12. 638–644. 10 indexed citations
17.
Cristianini, Nello, John Shawe‐Taylor, & Peter Sykacek. (1998). Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space. ePrints Soton (University of Southampton). 109–117. 7 indexed citations
18.
Sykacek, Peter. (1998). Outliers and Bayesian Inference. Natural Computing. 973–978. 2 indexed citations
19.
Sykacek, Peter, Georg Dorffner, Peter Rappelsberger, & J. Zeitlhofer. (1997). Experiences with Bayesian Learning in a Real World Application. Neural Information Processing Systems. 10. 964–970.
20.
Sykacek, Peter. (1997). Equivalent error bars for neural network classifiers trained by Bayesian inference.. The European Symposium on Artificial Neural Networks. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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